flopscope.numpy.union1d
fnp.union1d(ar1, ar2)[flopscope source][numpy source]
Find the union of two arrays.
Adapted from NumPy docs np.union1d
Cost
per-operation
Flopscope Context
Set union; cost = (n+m)*ceil(log2(n+m)).
Return the unique, sorted array of values that are in either of the two input arrays.
Parameters
- ar1, ar2:array_like
Input arrays. They are flattened if they are not already 1D.
Returns
- union1d:ndarray
Unique, sorted union of the input arrays.
Examples
>>> import flopscope.numpy as fnp
>>> flops.union1d([-1, 0, 1], [-2, 0, 2])
array([-2, -1, 0, 1, 2])To find the union of more than two arrays, use functools.reduce:
>>> from functools import reduce
>>> reduce(flops.union1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2]))
array([1, 2, 3, 4, 6])